code: def make_hifigan_multi_scale_multi_period_discriminator_args(
      **kwargs,
  ):
      defaults = dict(
          scales=2,
          scale_downsample_pooling="AvgPool1d",
          scale_downsample_pooling_params={
              "kernel_size": 4,
              "stride": 2,
              "padding": 2,
          },
          scale_discriminator_params={
              "in_channels": 1,
              "out_channels": 1,
              "kernel_sizes": [15, 41, 5, 3],
              "channels": 16,
              "max_downsample_channels": 16,
              "max_groups": 16,
              "bias": True,
              "downsample_scales": [2, 2],
              "nonlinear_activation": "LeakyReLU",
              "nonlinear_activation_params": {"negative_slope": 0.1},
          },
          follow_official_norm=False,
          periods=[2, 3],
          period_discriminator_params={
              "in_channels": 1,
              "out_channels": 1,
              "kernel_sizes": [5, 3],
              "channels": 8,
              "downsample_scales": [3, 3],
              "max_downsample_channels": 32,
              "bias": True,
              "nonlinear_activation": "LeakyReLU",
              "nonlinear_activation_params": {"negative_slope": 0.1},
              "use_weight_norm": True,
              "use_spectral_norm": False,
          },
      )
      defaults.update(kwargs)
      return defaults
  
file_path: espnet/test/espnet2/gan_tts/hifigan/test_hifigan.py
imports: import numpy
  import pytest
  import torch
  from espnet2.gan_tts.hifigan import HiFiGANGenerator, HiFiGANMultiScaleMultiPeriodDiscriminator
  from espnet2.gan_tts.hifigan.loss import DiscriminatorAdversarialLoss, FeatureMatchLoss, GeneratorAdversarialLoss, MelSpectrogramLoss
label_desc: Use image node_id 2 for calling a global function with example usage: make_hifigan_multi_scale_multi_period_discriminator_args() and returns: defaults